Original ArticleAssociation of Socioeconomic Area Deprivation Index with Hospital Readmissions After Colon and Rectal Surgery
Introduction
Colorectal operations are among the most common surgeries performed in the USA and are associated with one of the highest rates of complications and hospital readmissions of general surgery procedures.1,2 The Hospital Readmissions Reduction Program (HRRP) mandates financial penalties for higher than expected 30-day readmission rates. The HRRP’s unintended consequences of disproportionately penalizing safety net hospitals (SNHs) constrain resources at hospitals caring for the most vulnerable populations.3–7 A study across four states showed a 30-day readmission rate for colectomy patients of 10.9% that increased to 12.1% in the subgroup of hospitals with a high safety net burden.8 The Centers for Medicare & Medicaid Services (CMS) recently established a new peer group-based payment adjustment system for assessing readmission penalties comparing similar hospitals stratified by patient socioeconomic status (SES) measured by the proportion of patients with dual enrollment in Medicare/Medicaid. The greatest penalty reductions occurred in hospitals serving the lowest SES patients, lessening, but not eliminating, the previously unbalanced penalty burden for SNH.9
Patient-level SES variables that could improve risk adjustment are limited in administrative data and electronic health records (EHR). However, and despite known limitations, dual enrollment in Medicare/Medicaid, insurances status,2 and race and ethnicity are commonly used measures to identify health disparities and low SES populations.10 Another commonly used patient characteristic is their zip code linked to US Census data to estimate individual-level SES, usually showing no or mixed effects on outcomes11–15 due to the lack of granularity. US Census Zip Code Tabulation Areas are generalized representations of zip codes with an average population of 9414. The National Academy of Medicine released five reports outlining the need to develop better risk-adjustment methods using more granular social risk factor data.16 A more granular proxy measure is the “block group” representing approximately 1431 residents.17
The Area Deprivation Index (ADI) and Distressed Communities Index (DCI) were developed to help identify populations living in deprived/disadvantaged neighborhoods. The ADI18 uses 17 American Community Survey19 variables from the US Census to stratify geographic areas based on socioeconomic disadvantage at the more granular block group level. Patients living in more compared with less disadvantaged neighborhoods had higher 30-day all-cause readmissions.18,20,21 The DCI combines five measures from the American Community Survey and two measures from the US Census Bureau Business Patterns dataset to estimate the community socioeconomic distress by zip codes.22 Several recent studies merged DCI with the American College of Surgeons National Quality Improvement Program (NSQIP)23–26 data improving prediction of postoperative complications and risk adjustment.27–31 Using ADI data provides more granularity but requires patient addresses to be geocoded, while DCI can be easily linked to patients using their zip codes but provides data on a less granular level.
Comparing studies on the effect of social risk factors on readmission risk is further complicated by the variability in defining 30-day readmissions. Many studies using NSQIP data define readmissions as 30 days from the date of surgery while the CMS defines readmissions as 30 days from the date of discharge from the index procedure. The goals of our study are to (1) examine ADI, DCI, and insurance status as proxy SES measures to predict 30-day readmissions after colorectal surgery and (2) use both definitions of 30-day readmissions in models using ADI, DCI, and insurance status. We hypothesize that patients living in distressed/deprived neighborhoods have an increased risk of 30-day readmissions and that using the more granular ADI will provide increased sensitivity in predicting readmission risk than the DCI or insurance status.
Section snippets
Patient Population and Variables
All patients undergoing colorectal procedures present in the 2013–2017 NSQIP of University Hospital,32 a safety net county hospital, were evaluated for study inclusion (Fig. 1). NSQIP registry was used for cohort identification as well providing standardized definitions of preoperative risk factors and complications.24–26 Colorectal procedures were identified using the NSQIP Principle Current Procedural Terminology (CPT) code for the index surgery and categorized as laparoscopic, open,
Characteristics of Colorectal Surgery Patients
Our sample consisted of 728 patients undergoing 746 colorectal procedures at the University Hospital present in the 2013–2017 NSQIP; 18 patients had two procedures. Patients were excluded for (1) addresses that could not be geocoded (n = 25), (2) ADI was not assigned to their block group (n = 10), (3) expiring during the index procedure hospitalization (no chance of readmission, n = 13), or (4) expiring within 30 days of the discharge date without being readmitted (n = 3), leaving 677 patients
Discussion
Our study demonstrates an association of proxy social risk factors with increased risk of 30-day readmissions using the ADI, a block group level measure of neighborhood deprivation. We examined three measures used to identify low SES patients, ADI, DCI, and insurance status. Only ADI was associated with higher 30-day readmission rates from both the dates of surgery and discharge in low SES patients (Table 8). Our study is the first to apply the ADI to outcomes in patients undergoing colorectal
Conclusions
The ADI identified patients living in deprived communities with increased readmission risk with increased sensitivity compared to the DCI and insurance status. Our results show that ADI can potentially be used (1) for risk adjustment to improve predictive models at an institutional level, (2) to identify high-risk patients, and (3) to design better care pathways that improve health outcomes.
Funding Information
This work was supported by National Institutes of Health grants U01TR002393, UL1TR002645, and T32HL07446.
Disclaimer
The views expressed are those of the authors and do not represent the views of the National Institutes of Health. The National Institutes of Health did not play any role in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.
Conflict of Interest
The authors declare that they have no conflicts of interest.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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2023, Journal of Surgical ResearchCitation Excerpt :As such, each health care system should assess outcome differences for their unique patient populations, including insurance type. Third, insurance type/status is a patient-level social risk factor, other risk factors such as neighborhood deprivation44,66,86 and financial strain87,88 contribute to surgical outcomes. Fourth, we used complications available in NSQIP, various surgeries have specific complications, not in NSQIP.
Meeting Presentation: Presented at the Academic Surgical Congress, Orlando, FL, February 2020